Current computer hardware resources and associated techniques provide for fast and efficient collection and storage of large amounts of data. However, it may be difficult for providers and consumers of such data to make optimal use of the information contained therein. For example, it may be difficult to discern patterns and relationships within and among the stored data.
Frequently, information within such data may be stored within discrete content files, e.g., text documents, image files, video files, or audio files. In such cases, use of the information stored within the various content files may be facilitated by clustering related content files within larger groups or sets of content files. For example, it may be useful to cluster groups of content files according to some shared characteristic, e.g., clustering content files based on a similarity of subject matter, similarity of source/origin, or virtually any other feature or characteristic of the content file which the user may wish to utilize as basis for grouping content files into clusters.
Accordingly, a number of conventional clustering algorithms are known which may be used to execute an automatic clustering of content files within a group of designated content files, e.g., based on a set of parameters or features provided by an operator of the clustering algorithm in question. In practice, however, such clustering algorithms may be insufficient to meet the needs of the user in accessing or otherwise utilizing the designated group of content files. Consequently, the user may fail to receive the full benefit of information available within the group of content files, and/or may fail to receive a desired access or other benefit within a desired timeframe. Thus, the utilization of the information stored within the content files may be suboptimal.